{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dagstermill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "context = dagstermill.get_context()\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-06 15:09:03 - dagster - INFO - system - ba7e9434-71a4-4e4b-8979-8ad5c382fe67 - Fitted linear regression model!\n"
     ]
    }
   ],
   "source": [
    "X = df[['sepal_length', 'sepal_width']]\n",
    "y = df['petal_width']\n",
    "model = LinearRegression()\n",
    "fit = model.fit(X,y)\n",
    "context.log.info(\"Fitted linear regression model!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-03-06 15:09:04 - dagster - INFO - system - ba7e9434-71a4-4e4b-8979-8ad5c382fe67 - Linear regression model has score 0.7429280023779846\n"
     ]
    }
   ],
   "source": [
    "score = fit.score(X, y)\n",
    "context.log.info(\"Linear regression model has score {}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = fit.predict(X)\n",
    "plt.scatter(y, pred_y)\n",
    "plt.title(\"True vs. predicted\")\n",
    "plt.xlabel(\"True petal width\")\n",
    "plt.ylabel(\"Predicted petal width\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "dagster",
   "language": "python",
   "name": "dagster"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
